Towards Failure-Based Instructional Design: A Phenomenological Study of the Perceptions of Drone Pilots About the Use of Simulations to Promote Failure-Based Learning

Simulations have become increasingly popular in many contexts, particularly for performance optimization, testing, and safety (Aldrich, 2003). By nature, simulations immerse the learner in an environment that is an approximate imitation of the situation or process to be learned (Baek, 2009). In the literature, there is a lack of qualitative research on the perceptions of learners regarding the use of failure-based learning in simulations. The idea of learning through failure experiences is not a new concept, yet, to date, no instructional design models have discussed how to employ failure strategically within education (Tawfik, Rong, & Choi, 2015). This study utilized Tawfik et al.’s (2015) unified model of failure and learning systems design to create a drone flight simulation designed to focus on safely operating a drone while capturing high-quality aerial videography. Data collection included semi-structured interviews with 16 licensed drone pilots. This study illuminates the pilots’ perceptions and understanding about employing a failure-based learning model in a drone flight training simulation. Key findings from a thematic analysis of the interviews were that learners find value in experiencing and learning from failure and that the failure experiences led to increased self-confidence and intrinsic motivation

Capable but Amoral? Comparing AI and Human Expert Collaboration in Ethical Decision Making

While artificial intelligence (AI) is increasingly applied for decision- making processes, ethical decisions pose challenges for AI applica- tions. Given that humans cannot always agree on the right thing to do, how would ethical decision-making by AI systems be perceived and how would responsibility be ascribed in human-AI collabora- tion? In this study, we investigate how the expert type (human vs. AI) and level of expert autonomy (adviser vs. decider) influence trust, perceived responsibility, and reliance. We find that partici- pants consider humans to be more morally trustworthy but less capable than their AI equivalent. This shows in participants’ re- liance on AI: AI recommendations and decisions are accepted more often than the human expert’s. However, AI team experts are per- ceived to be less responsible than humans, while programmers and sellers of AI systems are deemed partially responsible instead

Exploring the Effects of Experience on Drone Piloting

abstract: The current study aims to explore factors affecting trust in human-drone collaboration. A current gap exists in research surrounding civilian drone use and the role of trust in human-drone interaction and collaboration. Specifically, existing research lacks an explanation of the relationship between drone pilot experience, trust, and trust-related behaviors as well as other factors. Using two dimensions of trust in human-automation team—purpose and performance—the effects of experience on drone design and trust is studied to explore factors that may contribute to such a model. An online survey was conducted to examine civilian drone operators’ experience, familiarity, expertise, and trust in commercially available drones. It was predicted that factors of prior experience (familiarity, self-reported expertise) would have a significant effect on trust in drones. The choice to use or exclude the drone propellers in a search-and-identify scenario, paired with the pilots’ experience with drones, would further confirm the relevance of the trust dimensions of purpose versus performance in the human-drone relationship. If the pilot has a positive sense of purpose and benevolence with the drone, the pilot trusts the drone has a positive intent towards them and the task. If the pilot has trust in the performance of the drone, they ascertain that the drone has the skill to do the task. The researcher found no significant differences between mean trust scores across levels of familiarity, but did find some interaction between self-report expertise, familiarity, and trust. Future research should further explore more concrete measures of situational participant factors such as self-confidence and expertise to understand their role in civilian pilots’ trust in their drone.Dissertation/ThesisMasters Thesis Human Systems Engineering 201

A framework for the implementation of drones in German automotive OEM logistics operations

Intralogistics operations in automotive OEMs increasingly confront problems of overcomplexity caused by a customer-centred production that requires customisation and, thus, high product variability, short-notice changes in orders and the handling of an overwhelming number of parts. To alleviate the pressure on intralogistics without sacrificing performance objectives, the speed and flexibility of logistical operations have to be increased. One approach to this is to utilise three-dimensional space through drone technology. This doctoral thesis aims at establishing a framework for implementing aerial drones in automotive OEM logistic operations. As of yet, there is no research on implementing drones in automotive OEM logistic operations. To contribute to filling this gap, this thesis develops a framework for Drone Implementation in Automotive Logistics Operations (DIALOOP) that allows for a close interaction between the strategic and the operative level and can lead automotive companies through a decision and selection process regarding drone technology. A preliminary version of the framework was developed on a theoretical basis and was then revised using qualitative-empirical data from semi-structured interviews with two groups of experts, i.e. drone experts and automotive experts. The drone expert interviews contributed a current overview of drone capabilities. The automotive experts interview were used to identify intralogistics operations in which drones can be implemented along with the performance measures that can be improved by drone usage. Furthermore, all interviews explored developments and changes with a foreseeable influence on drone implementation. The revised framework was then validated using participant validation interviews with automotive experts. The finalised framework defines a step-by-step process leading from strategic decisions and considerations over the identification of logistics processes suitable for drone implementation and the relevant performance measures to the choice of appropriate drone types based on a drone classification specifically developed in this thesis for an automotive context

Psychographic And Behavioral Segmentation Of Food Delivery Application Customers To Increase Intention To Use

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceThis study presents a framework for segmenting Food Delivery Application (FDA) customers based on psychographic and behavioral variables as an alternative to existing segmentation. Customer segments are proposed by applying clustering methods to primary data from an electronic survey. Psychographic and behavioral constructs are formulated as hypotheses based on existing literature, and then evaluated as segmentation variables regarding their discriminatory power for customer segmentation. Detected relevant variables are used in the application of clustering techniques to find adequate boundaries within customer groupings for segmentation purposes. Characterization of customer segments is performed and enriched with implications of findings in FDA marketing strategies. This paper contributes to theory by providing new findings on segmentation that are relevant for an online context. In addition, it contributes to practice by detailing implications of customer segments in an online sales strategy, allowing marketing managers and FDA businesses to capitalize knowledge in their conversion funnel designs

Human-Centered Explainable Artificial Intelligence for Anomaly Detection in Quality Inspection: A Collaborative Approach to Bridge the Gap Between Humans and AI

In the quality inspection industry, the use of Artificial Intelligence (AI) continues to advance to produce safer and faster autonomous systems that can perceive, learn, decide, and act independently. As observed by the researcher interacting with the local energy company over a one-year period, these AI systems’ performance is limited by the machine’s current inability to explain its decisions and actions to human users. Especially in energy companies, eXplainable-AI (XAI) is critical to achieve speed, reliability, and trustworthiness with human inspection workers. Placing humans alongside AI will establish a sense of trust that augments the individual’s capabilities at the workplace. To achieve such an XAI system centered around humans, it is necessary to design and develop more explainable AI models. Incorporating these XAI systems centered around human workers in the inspection industry brings a significant shift in conducting visual inspections. Adding this explainability factor to the AI intelligent inspection systems makes the decision-making process more sustainable and trustworthy by bringing a collaborative approach. Currently, there is a lack of trust between the inspection workers and AI, creating uncertainty among inspection workers about the use of the existing AI models. To address this gap, the purpose of this qualitative research study was to explore and understand the need for human-centered XAI systems to detect anomalies in quality inspection in energy industries

Exploring Factors That Affect Usefulness, Ease Of Use, Trust, And Purchase Intention In The Online Environment

Various studies have examined the effects of factors on online attitudes and behavior. By applying the Technology Acceptance Model, this study is focused on investigating factors that affect customers’ online purchasing behavior. In particular, this study examines i) effects of such factors as product information, price, convenience, and perceived product or service quality on perceived usefulness; ii) effects of convenience, perceived product or service quality, and desire to shop without a salesperson on perceived ease of use; iii) effects of perceived ease of use on perceived usefulness; iv) effects of perceived ease of use and usefulness on intentions to shop online; and v) effects of trust on purchase intentions. The data collected online and offline were analyzed using factor and regression analysis, and structural equation modeling. The results of this study indicate that perceived usefulness, perceived ease of use, and trust had a statistically significant effect on behavioral intention to shop on the Internet

Trusting the Moral Judgments of a Robot: Perceived Moral Competence and Humanlikeness of a GPT-3 Enabled AI

Advancements in computing power and foundational modeling have enabled artificial intelligence (AI) to respond to moral queries with surprising accuracy. This raises the question of whether we trust AI to influence human moral decision-making, so far, a uniquely human activity. We explored how a machine agent trained to respond to moral queries (Delphi, Jiang et al., 2021) is perceived by human questioners. Participants were tasked with querying the agent with the goal of figuring out whether the agent, presented as a humanlike robot or a web client, was morally competent and could be trusted. Participants rated the moral competence and perceived morality of both agents as high yet found it lacking because it could not provide justifications for its moral judgments. While both agents were also rated highly on trustworthiness, participants had little intention to rely on such an agent in the future. This work presents an important first evaluation of a morally competent algorithm integrated with a human-like platform that could advance the development of moral robot advisors

Usability of Urban Air Mobility: Quantitative and Qualitative Assessments of Usage in Emergency Situations

The purpose of these four studies was to determine participants’ willingness to support the use of urban air mobility (UAM) in response to natural disasters, along with the preferred locations to establish vertiports. Study 1 assessed the willingness to support using a mixed factorial design. The findings demonstrated strong, robust support for the use of UAM when responding to natural disasters. Study 2 worked to create and validate a scale that could assess vertiports\u27 current and proposed locations. The Vertiport Usability Scale was developed and shown to have strong psychometric properties to validly assess vertiport locations through a multi-stage process. Study 3 used the Vertiport Usability Scale to understand the most highly preferred locations for vertiports in three conditions from a multi-stage process: temporary disaster locations, permanent disaster locations, and permanent consumer locations. Study 4 was conducted using qualitative methods to complement the earlier quantitative approaches. Through an initial survey and follow-on interview, three themes emerged related to UAM in response to natural disasters and vertiports: 1) human involvement in UAM operations, 2) scenarios for usage, and 3) setup and deployment of vehicles

Environmental enforcement technology adoption: Marine environment in the Kingdom of Bahrain

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonResearch Background: Entities responsible for environmental enforcement around the world are facing increasing pressure to deal with the alarming rate of environmental degradation and crime, despite the limited resources available to them. They are continuously searching for technological advancements to enhance their effectiveness and efficiency. This research aims to explore and develop effective technology adoption insights for environmental compliance and enforcement in the Kingdom of Bahrain. Research Gap: Existing technology adoption studies in law enforcement were mainly conducted on criminal law enforcement. Studies conducted on technology adoption of users involved in environmental enforcement were very scarce, especially from the perspective of environmental enforcers. Environmental enforcement was found to be a unique field of law enforcement with unique challenges and characteristics. This gap in the literature gives interest to acquire further knowledge and understanding of the technology use and adoption of users involved in environmental enforcement. Methodology: This study has adapted a qualitative approach with multiple case studies triangulated with onsite observations, documents, and interviews with relevant stakeholders to explore the use and adoption of technology in environmental compliance and enforcement in the Kingdom of Bahrain. Findings: Numerous significant findings were made during the research. The research found that technology use in the field of environmental enforcement in the marine environment in Bahrain has a significant impact on the effectiveness and efficiency of these systems, mainly through providing increased accuracy, speed, cost reduction and transparency. The technologies vary from basic technologies such as GPS to complex integrated systems, the type of technology utilized is mainly driven by the role and task of the organization or individual. The research also finds many aspects of theories from previous studies on technology adoption in the field of law enforcement to be applicable to this research’s field, mainly the M-TAM which is based on the TAM model. The research found that technology adoption in environmental enforcement in the Kingdom is mandatory in nature, in which intent to use the technology has no influence since the user does not have the choice not to use the technology provided. Rather the extent to which the technology is used is influenced mainly by attitude, perceived usefulness, degree of necessity of the use of the technology, and various external factors (management style, cognitive acceptance, and stakeholder environment). Furthermore, the research identified additional unique factors affecting technology adoption in environmental enforcement, mainly the stakeholder’s environment. The study's findings led to the development of a modified model of technology adoption for environmental enforcement. The model illustrates the complex interaction between many factors influencing users' adoption and usage of technology in the field of environmental enforcement in the Kingdom of Bahrain. Implications: The model illustrates the factors that influence technology adoption by users involved in environmental enforcement in the Kingdom of Bahrain. The main contribution of the research and the model is the ability to predict and explain technology adoption of users in the unique field of environmental enforcement. This knowledge is critical for the Kingdom, which has made significant investments in technological solutions across a variety of sectors, including environmental enforcement. Understanding and taking into account the factors influencing technology adoption will increase the likelihood of effective technology being adopted successfully in the Kingdom of Bahrain, resulting in more efficient usage, increased productivity, and ultimately improved performance toward the achievement of goals and targets.Ministry of Interior of the Kingdom of Bahrai